According to @karpathy, building large language models (LLMs) like ChatGPT involves a systematic process that includes data collection, model architecture design, large-scale training, and deployment. Karpathy emphasizes starting with massive, high-quality text datasets for pretraining, leveraging transformer-based architectures, and employing distributed training on powerful GPU clusters to achieve state-of-the-art results (Source: @karpathy via X.com). For practical applications, he highlights the importance of fine-tuning on domain-specific data to enhance performance in targeted business use-cases such as customer support automation, code generation, and content creation. This step-by-step methodology offers substantial opportunities for organizations looking to develop proprietary AI solutions and differentiate in competitive markets (Source: @karpathy, 2024).